Addressing Inequities in Urban Health: Do Decision-Makers Have the Data They Need? Report from the Urban Health Data Special Session at International Conference on Urban Health Dhaka 2015

Elsey, Helen; Thomson, Dana R.; Lin, Runtian; Maharjan, Uden; Agarwal, Siddharth; Newell, James

doi:10.1007/s11524-016-0046-9

Cited by 39 publications

(57 citation statements)

References 10 publications

(13 reference statements)

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“…The greater similarity of the cluster in terms of the population provides a more equal probability of selection, increasing the efficiency of the design and decreasing overall survey costs. This approach has also the advantage over other gridded population sampling approaches in which grid cells were selected with PPS then PSUs were "grown" by adding neighbouring grid cells after selection [11,32] as our approach produces sampling units with preferable population size and area before carrying out the sample selection. The quadtree algorithm relies on gridded population data to generate the population sampling frame as well as data on uncrossable boundaries and design stratifications (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Afghanistan has not conducted a full national census since 1979 [6], and Somalia since 1987 [7]. Where full census data are not available in a country, gridded population datasets have emerged over the last decade as a potential alternative to building household survey sampling frames [8][9][10][11]. Gridded population data are usually produced by models to give estimate counts of population density in uniform grid cells.…”

Section: Introductionmentioning

confidence: 99%

“…Other surveys have sampled small grid cells (e.g. 100 m × 100 m) and included neighbouring areas after initial cells were selected to ensure a minimum population per sampling unit [11]. This approach requires the calculation of a complex adaptive sample probability weight or risks incorrect weights and estimates [31].…”

Section: Introductionmentioning

confidence: 99%

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Using gridded population and quadtree sampling units to support survey sample design in low-income settings

et al. 2020

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Background: Household surveys are the main source of demographic, health and socioeconomic data in low-and middle-income countries (LMICs). To conduct such a survey, census population information mapped into enumeration areas (EAs) typically serves a sampling frame from which to generate a random sample. However, the use of census information to generate this sample frame can be problematic as in many LMIC contexts, such data are often outdated or incomplete, potentially introducing coverage issues into the sample frame. Increasingly, where census data are outdated or unavailable, modelled population datasets in the gridded form are being used to create household survey sampling frames. Methods: Previously this process was done by either sampling from a set of the uniform grid cells (UGC) which are then manually subdivided to achieve the desired population size, or by sampling very small grid cells then aggregating cells into larger units to achieve a minimum population per survey cluster. The former approach is time and resource-intensive as well as results in substantial heterogeneity in the output sampling units, while the latter can complicate the calculation of unbiased sampling weights. Using the context of Somalia, which has not had a full census since 1987, we implemented a quadtree algorithm for the first time to create a population sampling frame. The approach uses gridded population estimates and it is based on the idea of a quadtree decomposition in which an area successively subdivided into four equal size quadrants, until the content of each quadrant is homogenous. Results: The quadtree approach used here produced much more homogeneous sampling units than the UGC (1 × 1 km and 3 × 3 km) approach. At the national and prewar regional scale, the standard deviation and coefficient of variation, as indications of homogeneity, were calculated for the output sampling units using quadtree and UGC 1 × 1 km and 3 × 3 km approaches to create the sampling frame and the results showed outstanding performance for quadtree approach. Conclusion: Our approach reduces the manual burden of manually subdividing UGC into highly populated areas, while allowing for correct calculation of sampling weights. The algorithm produces a relatively homogenous population counts within the sampling units, reducing the variation in the weights and improving the precision of the resulting estimates. Furthermore, a protocol of creating approximately equal-sized blocks and using tablets for randomized

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using gridded population and quadtree sampling units to support survey sample design in low-income settings

et al. 2020

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“…This has proven to be an effective sample design when census EAs are the only available first-stage sample frame, maximizing statistical power while reducing field costs [55][56][57]. Two-stage sampling, however, requires that two field visits are made to each sampled household several months (or even years) apart, making it more likely that mobile and vulnerable households are excluded from the survey or fail to respond compared to stable long-term households [58]. This problem is of increasing concern in LMICs cities today as rates of urbanization and mobility increase [51], possibly leading to increased bias in standard two-stage household surveys.…”

Section: Discussionmentioning

confidence: 99%

Linking Synthetic Populations to Household Geolocations: A Demonstration in Namibia

Thomson

Kools

Jochem

2018

Data

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Whether evaluating gridded population dataset estimates (e.g., WorldPop, LandScan) or household survey sample designs, a population census linked to residential locations are needed. Geolocated census microdata data, however, are almost never available and are thus best simulated. In this paper, we simulate a close-to-reality population of individuals nested in households geolocated to realistic building locations. Using the R simPop package and ArcGIS, multiple realizations of a geolocated synthetic population are derived from the Namibia 2011 census 20% microdata sample, Namibia census enumeration area boundaries, Namibia 2013 Demographic and Health Survey (DHS), and dozens of spatial covariates derived from publicly available datasets. Realistic household latitude-longitude coordinates are manually generated based on public satellite imagery. Simulated households are linked to latitude-longitude coordinates by identifying distinct household types with multivariate k-means analysis and modelling a probability surface for each household type using Random Forest machine learning methods. We simulate five realizations of a synthetic population in Namibia’s Oshikoto region, including demographic, socioeconomic, and outcome characteristics at the level of household, woman, and child. Comparison of variables in the synthetic population were made with 2011 census 20% sample and 2013 DHS data by primary sampling unit/enumeration area. We found that synthetic population variable distributions matched observed observations and followed expected spatial patterns. We outline a novel process to simulate a close-to-reality microdata census geolocated to realistic building locations in a low- or middle-income country setting to support spatial demographic research and survey methodological development while avoiding disclosure risk of individuals.

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“…With no spatial data on such areas, survey samples and field data collection are more likely to underreport deprived communities in both national censuses and household surveys. However, if deprived area maps exist, deprivation indicators are generally diluted in urban averages when using administrative boundaries [32][33][34]. In Nairobi, for example, approximately 60% of the population currently live in deprived areas, which accounts for only about 4% of the built-up area for that city [35].…”

Section: Introductionmentioning

confidence: 99%

The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries

et al. 2020

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Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups.

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Addressing Inequities in Urban Health: Do Decision-Makers Have the Data They Need? Report from the Urban Health Data Special Session at International Conference on Urban Health Dhaka 2015

Cited by 39 publications

References 10 publications

Using gridded population and quadtree sampling units to support survey sample design in low-income settings

Using gridded population and quadtree sampling units to support survey sample design in low-income settings

Linking Synthetic Populations to Household Geolocations: A Demonstration in Namibia

The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries

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